Microbiological studies investigating mutagenicity of deep frying fat fractions and some of their components

In this study, the Salmonella/microsome mutagenicity test according to Ames et al. (Mutation Res. 31∶347, 1975) was performed in order to detect possible mutagenicity of oxidized deep frying fat fractions. Furthermore, the mono-, di-, tri- and tetrahydroxyoctadecanoic acids and the hydroperoxide of linoleic acid were investigated as model test substances. The Ames assay was carried out with and without metabolic activation including preincubation and liquid culture procedures as described by Mitchell (Mutation Res. 54∶1, 1978). The results show no mutagenic effects for the oxidized fractions of deep frying fats nor for the model test substances. At higher concentrations, however, limited test reliability resulted from direct toxic effects on bacterial growth.     

The Hunt for Natural Skin Whitening Agents

Skin whitening products are commercially available for cosmetic purposes in order to obtain a lighter skin appearance. They are also utilized for clinical treatment of pigmentary disorders such as melasma or postinflammatory hyperpigmentation. Whitening agents act at various levels of melanin production in the skin. Many of them are known as competitive inhibitors of tyrosinase, the key enzyme in melanogenesis. Others inhibit the maturation of this enzyme or the transport of pigment granules (melanosomes) from melanocytes to surrounding keratinocytes. In this review we present an overview of (natural) whitening products that may decrease skin pigmentation by their interference with the pigmentary processes.     

A double-layered carbon nanotube array with super-hydrophobicity

The growth of double-layered vertical-aligned carbon nanotube (CNT) arrays by a single step chemical vapor deposition is reported. The deactivation and reactivation of catalyst particles may be the cause of such a growth process. An interesting morphology difference between the top and the bottom CNT layers was observed. In contrast to the smooth surface of the top CNT layer, the surface of the bottom layer shows hierarchical structures. The surface structures of the bottom CNT layer allow this surface to exhibit super-hydrophobic properties and excellent self-cleaning abilities.     

Sport Stadia, Sporting Events and Urban Development: International Experience and the Ambitions of Durban

The paper considers the stadium construction and infrastructure projects underway for World Cup 2010. We ask whether and under which conditions hosting such an event (along with the new facilities and infrastructural development required) will have positive effects on urban development. By analysing the stadium project in Durban, the paper also presents evidence on whether such positive effects may be enhanced by the attempt to create an image effect by seeking outstanding architectural quality of the new stadia.

Top-Down Neural Attention by Excitation Backprop

We aim to model the top-down attention of a convolutional neural network (CNN) classifier for generating task-specific attention maps. Inspired by a top-down human visual attention model, we propose a new backpropagation scheme, called Excitation Backprop, to pass along top-down signals downwards in the network hierarchy via a probabilistic Winner-Take-All process. Furthermore, we introduce the concept of contrastive attention to make the top-down attention maps more discriminative. We show a theoretic connection between the proposed contrastive attention formulation and the Class Activation Map computation. Efficient implementation of Excitation Backprop for common neural network layers is also presented. In experiments, we visualize the evidence of a model’s classification decision by computing the proposed top-down attention maps. For quantitative evaluation, we report the accuracy of our method in weakly supervised localization tasks on the MS COCO, PASCAL VOC07 and ImageNet datasets. The usefulness of our method is further validated in the text-to-region association task. On the Flickr30k Entities dataset, we achieve promising performance in phrase localization by leveraging the top-down attention of a CNN model that has been trained on weakly labeled web images. Finally, we demonstrate applications of our method in model interpretation and data annotation assistance for facial expression analysis and medical imaging tasks.     

A survey of book recommender systems

The act of reading has benefits for individuals and societies, yet studies show that reading declines, especially among the young. Recommender systems can help stop such decline. We present a survey of recommender systems in the domain of books. We have categorized the systems into six classes, and highlighted the main trends, issues, evaluation approaches and datasets. Other research areas, such as psychology, are consulted to understand users’ books choices and reading models.     

Salient Object Subitizing

We study the problem of salient object subitizing, i.e. predicting the existence and the number of salient objects in an image using holistic cues. This task is inspired by the ability of people to quickly and accurately identify the number of items within the subitizing range (1–4). To this end, we present a salient object subitizing image dataset of about 14 K everyday images which are annotated using an online crowdsourcing marketplace. We show that using an end-to-end trained convolutional neural network (CNN) model, we achieve prediction accuracy comparable to human performance in identifying images with zero or one salient object. For images with multiple salient objects, our model also provides significantly better than chance performance without requiring any localization process. Moreover, we propose a method to improve the training of the CNN subitizing model by leveraging synthetic images. In experiments, we demonstrate the accuracy and generalizability of our CNN subitizing model and its applications in salient object detection and image retrieval.